


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Optional[int] 			=		(CMStochasticIterativeScheduler,)
			_UpperCamelCase		:       Union[str, Any] 			=		10




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							{
						    "num_train_timesteps": 201,
						    "sigma_min": 0.002,
						    "sigma_max": 80.0,
						}

						config.update(**snake_case  )
						return config




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							10

						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							self.scheduler_classes[0](**snake_case  )

						scheduler.set_timesteps(snake_case  )

						UpperCamelCase__       =							scheduler.timesteps[0]
						UpperCamelCase__       =							scheduler.timesteps[1]

						UpperCamelCase__       =							self.dummy_sample
						UpperCamelCase__       =							0.1 * sample

						UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample
						UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						self.assertEqual(output_a.shape   ,    sample.shape  )
						self.assertEqual(output_a.shape   ,    output_a.shape  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__       =							1
						scheduler.set_timesteps(snake_case  )
						UpperCamelCase__       =							scheduler.timesteps

						UpperCamelCase__       =							torch.manual_seed(0  )

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(snake_case  ):
									# 1. scale model input
									UpperCamelCase__       =							scheduler.scale_model_input(snake_case   ,    snake_case  )

									# 2. predict noise residual
									UpperCamelCase__       =							model(snake_case   ,    snake_case  )

									# 3. predict previous sample x_t-1
									UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case   ,    generator=snake_case  ).prev_sample

									UpperCamelCase__       =							pred_prev_sample

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 192.7614  ) < 1E-2
						assert abs(result_mean.item() - 0.2510  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__       =							[106, 0]
						scheduler.set_timesteps(timesteps=snake_case  )
						UpperCamelCase__       =							scheduler.timesteps

						UpperCamelCase__       =							torch.manual_seed(0  )

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
									# 1. scale model input
									UpperCamelCase__       =							scheduler.scale_model_input(snake_case   ,    snake_case  )

									# 2. predict noise residual
									UpperCamelCase__       =							model(snake_case   ,    snake_case  )

									# 3. predict previous sample x_t-1
									UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case   ,    generator=snake_case  ).prev_sample

									UpperCamelCase__       =							pred_prev_sample

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 347.6357  ) < 1E-2
						assert abs(result_mean.item() - 0.4527  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__       =							[39, 30, 12, 15, 0]

						with self.assertRaises(snake_case   ,    msg="`timesteps` must be in descending order."  ):
									scheduler.set_timesteps(timesteps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__       =							[39, 30, 12, 1, 0]
						UpperCamelCase__       =							len(snake_case  )

						with self.assertRaises(snake_case   ,    msg="Can only pass one of `num_inference_steps` or `timesteps`."  ):
									scheduler.set_timesteps(num_inference_steps=snake_case   ,    timesteps=snake_case  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__       =							[scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    snake_case   ,    msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"   ,    ):
									scheduler.set_timesteps(timesteps=snake_case  )






from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Tuple 			=		CustomTokenizer
			pass


